使用Java语言开发人工智能服务应用

目前市面上主流的深度学习框架 TensorFlow、pytorch、MxNet都是以Python语言为主，Java工程师们想要利用自己的优势开发一款深度学习应用绝非易事；通过本篇文章我们将解决这个问题，用极少的代码实现一个图片分类服务

场景

【物体分类】
通过Http请求，向后端服务传入一张图片地址，后端服务调用深度学习模型对图片进行处理，给出分类预测结果

安装本地库

以mxnet为例
首先下载本地库文件，根据机器配置选择下载，比我的GPU服务器
https://publish.djl.ai/mxnet-1.7.0-backport/win/cu102mkl/mxnet_61.dll.gz
win/mkl/libmxnet.dll.gz
win/common/libgcc_s_seh-1.dll.gz
win/common/libgfortran-3.dll.gz
win/common/libopenblas.dll.gz
win/common/libquadmath-0.dll.gz
将文件解压到 C:\Users\bigdata.djl.ai\mxnet

搭建工程

使用idea或者eclipse构建maven工程，并导入以下maven依赖

        <dependency>
            <groupId>commons-cli</groupId>
            <artifactId>commons-cli</artifactId>
            <version>1.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-slf4j-impl</artifactId>
            <version>2.12.1</version>
        </dependency>
        <dependency>
            <groupId>com.google.code.gson</groupId>
            <artifactId>gson</artifactId>
            <version>2.8.5</version>
        </dependency>
        <dependency>
            <groupId>ai.djl</groupId>
            <artifactId>api</artifactId>
            <version>${djl.version}</version>
        </dependency>
        <dependency>
            <groupId>ai.djl</groupId>
            <artifactId>basicdataset</artifactId>
            <version>${djl.version}</version>
        </dependency>
        <dependency>
            <groupId>ai.djl</groupId>
            <artifactId>model-zoo</artifactId>
            <version>${djl.version}</version>
        </dependency>

         <dependency>
            <groupId>com.sparkjava</groupId>
            <artifactId>spark-core</artifactId>
            <version>2.8.0</version>
        </dependency>
        <dependency>
            <groupId>ai.djl.mxnet</groupId>
            <artifactId>mxnet-model-zoo</artifactId>
            <version>${djl.version}</version>
        </dependency>
        <dependency>
            <groupId>ai.djl.mxnet</groupId>
            <artifactId>mxnet-engine</artifactId>
            <version>${djl.version}</version>
        </dependency>

        <dependency>
            <groupId>ai.djl.mxnet</groupId>
            <artifactId>mxnet-native-auto</artifactId>
            <version>1.7.0-a</version>
            <scope>runtime</scope>
        </dependency>

加载模型

使用djl ModelZoo加载ImageNet模型，并对输入图片进行分类预测

 
public class ImageNetTest {

    private static Predictor<BufferedImage, Classifications> predictor = null;

    static {
        load();
    }

    private static void load(){
        Criteria<BufferedImage, Classifications> criteria =
                Criteria.builder()
                        .optApplication(Application.CV.IMAGE_CLASSIFICATION)
                        .setTypes(BufferedImage.class, Classifications.class)
                        .optFilter("multiplier", "0.75")
                        .optFilter("flavor", "v1")
                        .optFilter("dataset", "imagenet")
                        .optArtifactId("mobilenet")
                        .optProgress(new ProgressBar())
                        .build();

        try {
            ZooModel<BufferedImage, Classifications> model = ModelZoo.loadModel(criteria);
            predictor =  model.newPredictor();
        } catch (IOException e) {
            e.printStackTrace();
        } catch (ModelNotFoundException e) {
            e.printStackTrace();
        } catch (MalformedModelException e) {
            e.printStackTrace();
        }
    }
    public static String predict(String imagePath) throws Exception {
        BufferedImage image;
        if (imagePath.startsWith("http")) {
            image = BufferedImageUtils.fromUrl(new URL(imagePath));
        } else {
            image = BufferedImageUtils.fromFile(Paths.get(imagePath));
        }
        return new Gson().toJson(predictor.predict(image).topK(3));
    }

    public static void main(String[] args)throws Exception  {
        System.out.println(predict("src/test/resources/dog-cat.jpg"));
    }
}

模型下载地址

下载模型 将文件解压到

{your_os_user_root}\.djl.ai\cache\repo\model\cv\image_classification\ai\djl\mxnet\mobilenet\v1\0.75

扫码下载

SimpleHttp

通过web spark 快速实现restful api

 public static void main(String[] args) {
        port(8899);
        get("/img_classes/predict", (request, response) -> {
            return ImageNetTest.predict(request.queryParams("img_url"));
        });
    }

测试效果

斗牛犬 image.png